Abstract
In this paper, an Artificial Bee Colony (ABC) metaheuristic is adapted to find Pareto optimal solutions set for Goal Programming (GP) Problems. At first, the GP model is converted to a multi-objective optimization problem (MOO) of minimizing deviations from fixed goals. At second, the ABC is personalized to support the MOO by means of a weighted sum formulation for the objective function: solving several scalarization of the objective function according to a weight vector with non-negative components. The efficiency of the proposed approach is demonstrated by nonlinear engineering design problems. In all problems, multiple solutions to the goal programming problem are found in short computational time using very few user-defined parameters.
Published Version
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